How to read these plots: Each panel shows how the effect of the ban unfolded over time in a single state. The line tracks the estimated effect at each month after the law took effect. The shaded band is a 95% confidence interval — if it includes zero, the effect at that time point is not statistically distinguishable from no change.

Why sex crimes matter: Confessions play an outsized role in sex crime cases. If deception bans affect case outcomes, the impact may be most visible here. These panels show the same month-by-month analysis, focused specifically on sex crime clearances.

What this tests: If these bans had no real effect, how unusual is the observed result? I randomly re-assign treatment timing 500 times and re-estimate the pooled effect each time. The histogram shows this “null distribution.” If the actual estimate (vertical line) falls within the bulk of the distribution, the real effect cannot be distinguished from random noise.

What this tests: I check for “effects” at fake treatment dates (6, 9, and 12 months before the actual ban). If the research design is valid, these placebo effects should be zero — indicating no pre-existing trend that could be mistaken for a real effect. Points near zero (gray shaded zone) support the design.
Note: IL and OR lack estimates at 12 months before the ban due to insufficient pre-treatment data at that lead.

What this shows: Each panel shows the distribution of agency-level treatment effects within a state. A distribution centered on zero means most agencies saw no change. Agency counts appear low because the analysis is restricted to agencies with enough juvenile incidents both before and after the ban to produce reliable estimates — most small or rural departments are excluded. States with shorter post-treatment windows (e.g., NV with ~6 months) have fewer qualifying agencies.